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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika SMATIKA Jurnal Ilmiah Universitas Batanghari Jambi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Komputasi dan Teknologi Informasi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Pengabdian Masyarakat Asia Jurnal Pengabdian Dharma Wacana JAPI: Jurnal Akses Pengabdian Indonesia JUSIFOR : Jurnal Sistem Informasi dan Informatika Journal of Internet and Software Engineering Journal of Computing Theories and Applications Journal of Information System and Application Development Smatika Jurnal : STIKI Informatika Jurnal
Anis Zubair, Anis
Sistem Informasi, Fakultas Teknologi Informasi, Universitas Merdeka Malang,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Perbandingan Double Exponential Smoothing, Single Exponential Smoothing dan MA terhadap Peramalan Jumlah Pelanggan Di Gendis Jowo Soetedja, Aryadhiva; Hidayati, Rahmatina; Zubair, Anis; Indana, Luthfi
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8896

Abstract

Gendis Jowo experiences fluctuations in the number of nasi box customers, which lead to suboptimal stock management and operational inefficiency, thereby requiring a forecasting approach to predict customer numbers more accurately. This study applies three forecasting methods Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Moving Average (MA)—with the aim of determining the most accurate method for forecasting the next period’s customer count. Historical data from January 2022 to August 2025 were analyzed, with SES and DES parameters optimized using the Optimal ARIMA approach, and accuracy evaluated through MAPE, MAD, and MSD. The results show that the Moving Average method with a length of 4 (MA4) provides the highest accuracy with the lowest error values, making it the best-performing model. Based on the MA4 method, the number of customers for the next period is predicted to be 1,065.88, and this result can be used to plan stock requirements, packaging needs, and operational activities more effectively.
Rebranding UMKM “produk olahan Aloe vera” di kelurahan Ciptomulyo Rahmatina Hidayati; Anis Zubair; Viry Puspaning Ramadhan; Iqbal Baghis Kenvin
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 1 (2024): March
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i1.15440

Abstract

Abstrak                                                                                  Di Kelurahan Ciptomulyo, Kecamatan. Sukun, Kota Malang terdapat UMKM yang bernama Warung Perempuan Mandiri. Salah satu kegiatan UMKM ini adalah membuat makanan dan minuman yang berbahan dasar Aloe vera (lidah buaya). Proses produksi olahan Aloe vera dilakukan ketika menerima pesanan. Namun, kegiatan ini terhenti semenjak pandemi Covid-19 tahun 2020. Padahal sumber dana UMKM Warung Perempuan Mandiri adalah dari hasil berjualan. Tim Pengabdi Universitas Merdeka Malang yang terdiri dari dosen dan mahasiswa membuat program kerja membangkitkan kembali produk olahan Aloe vera. UMKM mengolah Aloe vera menjadi minuman sari, sirup, dan serabi. Pada kemasan produk sebelumnya, belum terdapat logo dan label yang digunakan belum mencantumkan komposisi, serta masa berlaku produk. Dari permasalahan tersebut, Tim Pengabdi membuatkan design logo dan redesign label serta mengganti packaging. Kegiatan ini dinamakan dengan rebranding. Hasil dari pengabdian ini berupa logo, desain label baru, dan packaging baru. Kata kunci: rebranding; UMKM; lidah buaya AbstractIn Ciptomulyo Village, Kec. Sukun, Malang City has an UMKM called Warung Perempuan Mandiri. One of these UMKM activities is making food and drinks made from Aloe vera. The production process of Aloe vera products  is carried out when receiving an order. However, this activity has been stoped  since the Covid-19 pandemic in 2020. Even though the source of funds for the UMKM Mandiri Women Shop is from sales. The Merdeka University Malang Service Team consisting of lecturers and students created a work program to revive processed Aloe vera products. UMKM processes Aloe vera into juice, syrup and pancakes. On the previous product packaging, there was no logo and the label used did not include the composition and validity period of the product. From these problems, the Service Team made a logo design and redesigned the label and replaced the packaging. This activity is called rebranding. The results of this dedication are in the form of logo, new label designs, and new packaging. Keywords: rebranding; UMKM; Aloe vera
Classifying School Scope Using Deep Neural Networks Based on Students' Surrounding Living Environments Mochammad Daffa Putra Karyudi; Anis Zubair
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11739

Abstract

This research investigates school scope classification using Deep Neural Networks (DNN), focusing on students living environments and educational opportunities. By addressing the interplay of socioeconomic and educational factors, the study aims to develop an analytical framework for understanding how environmental contexts shape academic trajectories. The research provides a nuanced understanding of the importance of features in educational classification by developing DNN models based on Spearman's Rank Correlation Coefficient (SRCC). The methodology employs machine learning techniques, integrating data wrangling, exploratory analysis, and multiple DNN models with K-fold cross-validation. The study analyzes 677 student records from two schools. The research examined multiple model configurations. Results show that the 'All Data' model achieved 83.08% accuracy, the 'Top 5' model 81.54%, and the 'Non-Top 5' model 79.23%. The SRCC-based approach revealed that while top correlated features are important, additional variables significantly contribute to model performance. The study highlights the profound impact of family background, social environment, and educational contexts on school selection. Furthermore, it demonstrates DNN's capability to uncover intricate, non-linear relationships, offering actionable insights for policymakers to leverage machine learning's potential in developing targeted educational strategies.
Co-Authors AA Sudharmawan, AA Adha, Muhammad Nibras Aditya Hidayat Pratama Agung Adha, Muhammad Nibras ambu may tida Anak Agung Gede Arifin, Yulia Natasya Farah Diba Asri Samsiar Ilmananda Bambang Nurdewanto Bekti Prihatiningsih Edward Roland Sitompul Elta Sonalitha Elta Sonalitha Sonalitha Fridayanti, Dinda Ahsana Zahra Haris Satriyawan Hasugian, Elsa Dame Ripka Hoar, Wilhelmina Sonya Indana, Luthfi Iqbal Baghis Kenvin Kristuadji, Yesaya Arya Danar Lailil Muflikhah Mochammad Daffa Putra Karyudi Nanis Hairunisya Putri, Sephia Dwi Arma Rahmatina Hidayati Ramadhan, Viry Puspaning Rauda Umamit Rijanto, Jonathan Billy Sadili, Ach Savsavubun, Valentinus Soetedja, Aryadhiva Viry Puspaning Ramadhan Wulandari, Fadila Yuliati, Ninik Catur Endah